Sparse document representations have been widely used to retrieve relevant documents via exact lexical matching. Owing to the pre-computed inverted index, it supports fast ad-hoc search but incurs the vocabulary mismatch problem. Although recent neural ranking models using pre-trained language models can address this problem, they usually require expensive query inference costs, implying the trade-off between effectiveness and efficiency. Tackling the trade-off, we propose a novel uni-encoder ranking model, Sparse retriever using a Dual document Encoder (SpaDE), learning document representation via the dual encoder. Each encoder plays a central role in (i) adjusting the importance of terms to improve lexical matching and (ii) expanding additional terms to support semantic matching. Furthermore, our co-training strategy trains the dual encoder effectively and avoids unnecessary intervention in training each other. Experimental results on several benchmarks show that SpaDE outperforms existing uni-encoder ranking models.

翻译：摘要：稀疏文档表示已广泛应用于通过精确词汇匹配检索相关文档。由于预计算倒排索引的支持，这种方法能够快速进行临时搜索，但会导致词汇不匹配问题。尽管近年来使用预训练语言模型的神经排序模型可以解决这一问题，但它们通常需要昂贵的查询推理成本，这意味着效果与效率之间存在权衡。为解决这一权衡，我们提出了一种新颖的单编码器排序模型——基于双文档编码器的稀疏检索器（SpaDE），通过双编码器学习文档表示。每个编码器分别承担以下核心角色：(i) 调整词项重要性以改进词汇匹配，以及(ii) 扩展额外词项以支持语义匹配。此外，我们的协同训练策略能够有效训练双编码器，并避免彼此训练时的不必要干扰。在多个基准测试上的实验结果表明，SpaDE优于现有的单编码器排序模型。